*SYNOPSIS OF DAV PROJECT*¶

Project Name :-¶

      Analysis Of TCS (TATA CONSULTANCY SERVICES)

Members :-¶

  1. Chirag Verma
  2. Pankaj Chaudhary
  3. Monu Kumar

Problem Statement :-¶

We will have three dataset i.e.. TCS_stock_action.csv, TCS_stock_history.csv and TCS_stock_info.csv .We will analyze these datasets and try to find out that whether this stocks gives a good return or not.

"TCS_sgtock_history.csv" :-

Data columns (total 8 columns): Column Non-Null Count Dtype

0 Date 4463 non-null datetime64[ns] 1 Open 4463 non-null float64
2 High 4463 non-null float64
3 Low 4463 non-null float64
4 Close 4463 non-null float64
5 Volume 4463 non-null int64
6 Dividends 4463 non-null float64
7 Stock Splits 4463 non-null float64
dtypes: datetime64ns, float64(6), int64(1) memory usage: 279.1 KB

"TCS_stock_action.csv" :-

Data columns (total 3 columns): Column Non-Null Count Dtype

0 Date 70 non-null object 1 Dividends 70 non-null float64 2 Stock Splits 70 non-null float64 dtypes: float64(2), object(1) memory usage: 1.8+ KB

Dataset Link :-¶

https://www.kaggle.com/datasets/0e540675cd717ee6d6a0a507a28cc1ca9d90d6b9948544f1f9b1bfffd30a1ab7

Description :-¶

Tata Consultancy Services (TCS) is an Indian multinational information technology (IT) services and consulting company headquartered in Mumbai, Maharashtra, India with its largest campus located in Chennai, Tamil Nadu, India. As of February 2021, TCS is the largest IT services company in the world by market capitalisation ($200 billion). It is a subsidiary of the Tata Group and operates in 149 locations across 46 countries.

TCS is the second largest Indian company by market capitalisation and is among the most valuable IT services brands worldwide.In 2015, TCS was ranked 64th overall in the Forbes World's Most Innovative Companies ranking, making it both the highest-ranked IT services company and the top Indian company. As of 2018, it is ranked eleventh on the Fortune India 500 list.In April 2018, TCS became the first Indian IT company to reach 100 billion dollar in market capitalisation and second Indian company ever (after Reliance Industries achieved it in 2007) after its market capitalisation stood at ₹6.793 trillion (equivalent to ₹7.3 trillion or US 100 billion dollar in 2019) on the Bombay Stock Exchange.

In 2016–2017, parent company Tata Sons owned 72.05% of TCS and more than 70% of Tata Sons' dividends were generated by TCS. In March 2018, Tata Sons decided to sell stocks of TCS worth 1.25 billion dollar in a bulk deal.As of 15 September 2021, TCS has recorded a market capitalisation of US$200 billion, making it the first Indian IT firm to do so.

Aim :-¶

The main aim of our project is to analyse the price of 'TCS' throughout the years and on basis of those we will predict future behaviour of this stock. On the basis of price and volume we try to find whether investors are likely to be more interested.

Requirements :-¶

• TCS Datasets.

• Jupyter Notebook or Google collab to perform Queries.

Queries based on dataset :-¶

  1. Details of csv file.
  2. Plotting price action graph.
  3. Analyzing price action during covid period.
  4. Finding the highest, lowest price in co
  5. Analyzing average return on investment from the start and in the covid crisis.
  6. Analyzing the market cap by calculating total traded money.
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import datetime as dt
# import yfinance
# import mplfinance as mpl
In [2]:
info=pd.read_csv("TCS_stock_info.csv")
info_df=pd.DataFrame(info)
info_df
Out[2]:
zip 400001
0 sector Technology
1 fullTimeEmployees 509058
2 longBusinessSummary Tata Consultancy Services Limited provides inf...
3 city Mumbai
4 phone 91 22 6778 9999
... ... ...
145 dividendYield 0.0105
146 bidSize 0
147 dayHigh 3805
148 regularMarketPrice 3773.2
149 logo_url https://logo.clearbit.com/tcs.com

150 rows × 2 columns

In [3]:
action=pd.read_csv("TCS_stock_action.csv")
action_df=pd.DataFrame(action)
action_df
Out[3]:
Date Dividends Stock Splits
0 2004-10-28 0.3750 0.0
1 2005-02-03 0.4375 0.0
2 2005-07-06 0.6250 0.0
3 2005-08-18 0.3750 0.0
4 2005-10-18 0.3750 0.0
... ... ... ...
65 2020-07-16 5.0000 0.0
66 2020-10-14 12.0000 0.0
67 2021-01-14 6.0000 0.0
68 2021-05-25 15.0000 0.0
69 2021-07-15 7.0000 0.0

70 rows × 3 columns

In [4]:
tcs=pd.read_csv("TCS_stock_history.csv")
df=pd.DataFrame(tcs)
df
Out[4]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-08-12 28.794172 29.742206 28.794172 29.519140 212976 0.0 0.0
1 2002-08-13 29.556316 30.030333 28.905705 29.119476 153576 0.0 0.0
2 2002-08-14 29.184536 29.184536 26.563503 27.111877 822776 0.0 0.0
3 2002-08-15 27.111877 27.111877 27.111877 27.111877 0 0.0 0.0
4 2002-08-16 26.972458 28.255089 26.582090 27.046812 811856 0.0 0.0
... ... ... ... ... ... ... ... ...
4458 2021-09-24 3890.000000 3944.399902 3855.000000 3871.300049 2320754 0.0 0.0
4459 2021-09-27 3900.000000 3904.000000 3802.899902 3836.949951 1673362 0.0 0.0
4460 2021-09-28 3850.000000 3850.000000 3751.250000 3779.149902 2253075 0.0 0.0
4461 2021-09-29 3759.800049 3806.000000 3722.149902 3791.899902 2489161 0.0 0.0
4462 2021-09-30 3805.000000 3805.000000 3765.000000 3773.199951 640479 0.0 0.0

4463 rows × 8 columns

In [5]:
df.describe()
Out[5]:
Open High Low Close Volume Dividends Stock Splits
count 4463.000000 4463.000000 4463.000000 4463.000000 4.463000e+03 4463.000000 4463.000000
mean 866.936239 876.675013 856.653850 866.537398 3.537876e+06 0.071533 0.001344
std 829.905368 838.267104 821.233477 829.611313 3.273531e+06 0.965401 0.051842
min 24.146938 27.102587 24.146938 26.377609 0.000000e+00 0.000000 0.000000
25% 188.951782 191.571816 185.979417 188.594620 1.860959e+06 0.000000 0.000000
50% 530.907530 534.751639 525.616849 529.713257 2.757742e+06 0.000000 0.000000
75% 1156.462421 1165.815854 1143.622800 1154.784851 4.278625e+06 0.000000 0.000000
max 3930.000000 3981.750000 3892.100098 3954.550049 8.806715e+07 40.000000 2.000000
In [6]:
print(np.ndim(df))

2
In [7]:
print(np.shape(df))

(4463, 8)
In [8]:
print(type(df))

<class 'pandas.core.frame.DataFrame'>
In [9]:
print(df.dtypes)

Date             object
Open            float64
High            float64
Low             float64
Close           float64
Volume            int64
Dividends       float64
Stock Splits    float64
dtype: object
In [44]:
df.head()
Out[44]:
Date Open High Low Close Volume Dividends Stock Splits year MarketCap MA50 MA200 Day
0 2002-08-12 28.794172 29.742206 28.794172 29.519140 212976 0.0 0.0 2002 6.132468e+06 NaN NaN 12
1 2002-08-13 29.556316 30.030333 28.905705 29.119476 153576 0.0 0.0 2002 4.539141e+06 NaN NaN 13
2 2002-08-14 29.184536 29.184536 26.563503 27.111877 822776 0.0 0.0 2002 2.401234e+07 NaN NaN 14
3 2002-08-15 27.111877 27.111877 27.111877 27.111877 0 0.0 0.0 2002 0.000000e+00 NaN NaN 15
4 2002-08-16 26.972458 28.255089 26.582090 27.046812 811856 0.0 0.0 2002 2.189775e+07 NaN NaN 16
In [45]:
df.tail()
Out[45]:
Date Open High Low Close Volume Dividends Stock Splits year MarketCap MA50 MA200 Day
4458 2021-09-24 3890.000000 3944.399902 3855.000000 3871.300049 2320754 0.0 0.0 2021 9.027733e+09 3525.432206 3206.583182 24
4459 2021-09-27 3900.000000 3904.000000 3802.899902 3836.949951 1673362 0.0 0.0 2021 6.526112e+09 3539.831006 3212.650094 27
4460 2021-09-28 3850.000000 3850.000000 3751.250000 3779.149902 2253075 0.0 0.0 2021 8.674339e+09 3552.279004 3218.446193 28
4461 2021-09-29 3759.800049 3806.000000 3722.149902 3791.899902 2489161 0.0 0.0 2021 9.358748e+09 3563.215005 3223.667407 29
4462 2021-09-30 3805.000000 3805.000000 3765.000000 3773.199951 640479 0.0 0.0 2021 2.437023e+09 3575.715005 3228.742965 30
In [11]:
df.columns
Out[11]:
Index(['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Dividends',
       'Stock Splits'],
      dtype='object')
In [12]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4463 entries, 0 to 4462
Data columns (total 8 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Date          4463 non-null   object 
 1   Open          4463 non-null   float64
 2   High          4463 non-null   float64
 3   Low           4463 non-null   float64
 4   Close         4463 non-null   float64
 5   Volume        4463 non-null   int64  
 6   Dividends     4463 non-null   float64
 7   Stock Splits  4463 non-null   float64
dtypes: float64(6), int64(1), object(1)
memory usage: 279.1+ KB
In [13]:
df.Date=pd.to_datetime(df.Date)
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4463 entries, 0 to 4462
Data columns (total 8 columns):
 #   Column        Non-Null Count  Dtype         
---  ------        --------------  -----         
 0   Date          4463 non-null   datetime64[ns]
 1   Open          4463 non-null   float64       
 2   High          4463 non-null   float64       
 3   Low           4463 non-null   float64       
 4   Close         4463 non-null   float64       
 5   Volume        4463 non-null   int64         
 6   Dividends     4463 non-null   float64       
 7   Stock Splits  4463 non-null   float64       
dtypes: datetime64[ns](1), float64(6), int64(1)
memory usage: 279.1 KB
In [14]:
df.tail()
Out[14]:
Date Open High Low Close Volume Dividends Stock Splits
4458 2021-09-24 3890.000000 3944.399902 3855.000000 3871.300049 2320754 0.0 0.0
4459 2021-09-27 3900.000000 3904.000000 3802.899902 3836.949951 1673362 0.0 0.0
4460 2021-09-28 3850.000000 3850.000000 3751.250000 3779.149902 2253075 0.0 0.0
4461 2021-09-29 3759.800049 3806.000000 3722.149902 3791.899902 2489161 0.0 0.0
4462 2021-09-30 3805.000000 3805.000000 3765.000000 3773.199951 640479 0.0 0.0
In [15]:
null_count=df.isnull().sum().sum()
print("Total Null Values:",null_count)

Total Null Values: 0
In [16]:
df['year'] = pd.DatetimeIndex(df['Date']).year
df.head()
Out[16]:
Date Open High Low Close Volume Dividends Stock Splits year
0 2002-08-12 28.794172 29.742206 28.794172 29.519140 212976 0.0 0.0 2002
1 2002-08-13 29.556316 30.030333 28.905705 29.119476 153576 0.0 0.0 2002
2 2002-08-14 29.184536 29.184536 26.563503 27.111877 822776 0.0 0.0 2002
3 2002-08-15 27.111877 27.111877 27.111877 27.111877 0 0.0 0.0 2002
4 2002-08-16 26.972458 28.255089 26.582090 27.046812 811856 0.0 0.0 2002
In [17]:
fig=go.Figure(data=go.Scatter(x=df['Date'],y=df['Close']))
fig.show()
In [18]:
px.scatter(df,x='Open',y='Close',color='High',size="Volume")
In [19]:
df['MarketCap'] = df['Open'] * df['Volume']
df
Out[19]:
Date Open High Low Close Volume Dividends Stock Splits year MarketCap
0 2002-08-12 28.794172 29.742206 28.794172 29.519140 212976 0.0 0.0 2002 6.132468e+06
1 2002-08-13 29.556316 30.030333 28.905705 29.119476 153576 0.0 0.0 2002 4.539141e+06
2 2002-08-14 29.184536 29.184536 26.563503 27.111877 822776 0.0 0.0 2002 2.401234e+07
3 2002-08-15 27.111877 27.111877 27.111877 27.111877 0 0.0 0.0 2002 0.000000e+00
4 2002-08-16 26.972458 28.255089 26.582090 27.046812 811856 0.0 0.0 2002 2.189775e+07
... ... ... ... ... ... ... ... ... ... ...
4458 2021-09-24 3890.000000 3944.399902 3855.000000 3871.300049 2320754 0.0 0.0 2021 9.027733e+09
4459 2021-09-27 3900.000000 3904.000000 3802.899902 3836.949951 1673362 0.0 0.0 2021 6.526112e+09
4460 2021-09-28 3850.000000 3850.000000 3751.250000 3779.149902 2253075 0.0 0.0 2021 8.674339e+09
4461 2021-09-29 3759.800049 3806.000000 3722.149902 3791.899902 2489161 0.0 0.0 2021 9.358748e+09
4462 2021-09-30 3805.000000 3805.000000 3765.000000 3773.199951 640479 0.0 0.0 2021 2.437023e+09

4463 rows × 10 columns

In [20]:
px.scatter(df,x='year',y='MarketCap',color='year')

Moving Averages

As we know the stock prices are highly volatile and prices change quickly with time. To observe any trend or pattern we can take the help of a 50-day 200-day average

In [21]:
df['MA50'] = df['Open'].rolling(50).mean()
df['MA200'] = df['Open'].rolling(200).mean()
df['Open'].plot(figsize = (20,10))
df['MA50'].plot()
df['MA200'].plot()
Out[21]:
<AxesSubplot: >
In [22]:
px.histogram(df,x='Volume',color="year",barmode="group")
In [23]:
px.box(df,y="High")
In [24]:
px.box(df,y="Open")
In [25]:
px.box(df,y="Close")
In [26]:
df1=df[df["year"]==2020]
df1['Month'] = pd.DatetimeIndex(df1['Date']).month_name()
df1.tail()

C:\Users\Dell\AppData\Local\Temp\ipykernel_13040\2410078670.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[26]:
Date Open High Low Close Volume Dividends Stock Splits year MarketCap MA50 MA200 Month
4273 2020-12-24 2883.049754 2895.289680 2857.479859 2883.396729 1807144 0.0 0.0 2020 5.210086e+09 2704.686069 2240.879458 December
4274 2020-12-28 2884.040737 2923.386536 2876.112102 2903.267578 2108994 0.0 0.0 2020 6.082425e+09 2706.033939 2245.075523 December
4275 2020-12-29 2893.951334 2925.665869 2893.951334 2904.357666 1994151 0.0 0.0 2020 5.770976e+09 2708.194486 2249.433227 December
4276 2020-12-30 2908.222868 2921.404270 2876.111997 2883.346924 2637968 0.0 0.0 2020 7.671799e+09 2711.847599 2254.554294 December
4277 2020-12-31 2874.129861 2879.085257 2819.620501 2837.212158 4040956 0.0 0.0 2020 1.161423e+10 2714.345122 2259.646221 December
In [27]:
fig=go.Figure(data=go.Scatter(x=df1['Date'],y=df1['High']))
fig.show()
In [28]:
max_price=max(df1['High'])
max_price
Out[28]:
2925.665869332239
In [29]:
min_price=min(df1['Low'])
min_price
Out[29]:
1467.8803591696203
In [30]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["High"],
                    mode='lines',
                    name='High'))
fig.add_trace(go.Scatter(x=df1["Date"], y=df1["Low"],
                    mode='lines',
                    name='Low'))


fig.show()
In [31]:
fig1 = go.Figure()
fig1.add_trace(go.Scatter(x=df1["Date"], y=df1["Open"],
                    mode='lines', name='Open'))
fig1.add_trace(go.Scatter(x=df1["Date"], y=df1["Close"],
                    mode='lines', name='Close'))

fig1.show()
In [32]:
px.histogram(df1,x='Volume',color="Month",barmode="group")
In [33]:
df1['MA50'] = df1['Open'].rolling(50).mean()
df1['MA200'] = df1['Open'].rolling(200).mean()
df1['Open'].plot(figsize = (20,10))
df1['MA50'].plot()
df1['MA200'].plot()

C:\Users\Dell\AppData\Local\Temp\ipykernel_13040\2359836275.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\Dell\AppData\Local\Temp\ipykernel_13040\2359836275.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[33]:
<AxesSubplot: >
In [34]:
px.scatter_matrix(df1,dimensions=["Open","Close","High","Low","Volume"],color="Month")
In [35]:
len(df)
Out[35]:
4463
In [36]:
df['Day'] = pd.DatetimeIndex(df['Date']).day
df.tail()
Out[36]:
Date Open High Low Close Volume Dividends Stock Splits year MarketCap MA50 MA200 Day
4458 2021-09-24 3890.000000 3944.399902 3855.000000 3871.300049 2320754 0.0 0.0 2021 9.027733e+09 3525.432206 3206.583182 24
4459 2021-09-27 3900.000000 3904.000000 3802.899902 3836.949951 1673362 0.0 0.0 2021 6.526112e+09 3539.831006 3212.650094 27
4460 2021-09-28 3850.000000 3850.000000 3751.250000 3779.149902 2253075 0.0 0.0 2021 8.674339e+09 3552.279004 3218.446193 28
4461 2021-09-29 3759.800049 3806.000000 3722.149902 3791.899902 2489161 0.0 0.0 2021 9.358748e+09 3563.215005 3223.667407 29
4462 2021-09-30 3805.000000 3805.000000 3765.000000 3773.199951 640479 0.0 0.0 2021 2.437023e+09 3575.715005 3228.742965 30
In [37]:
#Here we are calculating ROI(Return on investment) from start to end


sumTCS=0 #total amount invested in TCS

s1=0 #number of shares owned by TCS

#calcuating total amount invested and number of shares owned in TCS

for i in range(len(df)):

    if df.loc[i,'Day']==30:

        sumTCS+=df.loc[i,'Open']

        s1+=1

#displaying basic results

print("Total Invested in TCS = Rs",round(sumTCS,2))

print("Shares Owned of TCS =",s1)

print("Average Investmentment of 1 share = Rs",round((sumTCS/s1),2))



TCS_end=3805 #last open price of TCS on 2021-09-30


#calculating investment results

result1=round((TCS_end*s1)-sumTCS,2)

ROI_TCS=round((result1/sumTCS)*100,2)


#displaying investment results

print("\nInvestment Result:")


if result1<0:

    print("Net Unrealised Loss = Rs",result1)

else:

    print("Net Unrealised Profit = Rs",result1)



print("TCS ROI from 2000-1-3 to 2021-04-30 =",ROI_TCS,"%")

Total Invested in TCS = Rs 124202.71
Shares Owned of TCS = 138
Average Investmentment of 1 share = Rs 900.02

Investment Result:
Net Unrealised Profit = Rs 400887.29
TCS ROI from 2000-1-3 to 2021-04-30 = 322.77 %
In [38]:
df1
Out[38]:
Date Open High Low Close Volume Dividends Stock Splits year MarketCap MA50 MA200 Month
4028 2020-01-01 2108.266570 2123.728393 2094.652302 2107.877686 1354908 0.0 0.0 2020 2.856507e+09 NaN NaN January
4029 2020-01-02 2119.886798 2119.886798 2089.984038 2098.201172 2380752 0.0 0.0 2020 5.046925e+09 NaN NaN January
4030 2020-01-03 2104.376494 2161.750899 2104.376494 2140.016602 4655761 0.0 0.0 2020 9.797474e+09 NaN NaN January
4031 2020-01-06 2144.246950 2164.619679 2127.618001 2139.822266 3023209 0.0 0.0 2020 6.482507e+09 NaN NaN January
4032 2020-01-07 2139.870310 2153.630344 2123.630487 2145.072998 2429317 0.0 0.0 2020 5.198423e+09 NaN NaN January
... ... ... ... ... ... ... ... ... ... ... ... ... ...
4273 2020-12-24 2883.049754 2895.289680 2857.479859 2883.396729 1807144 0.0 0.0 2020 5.210086e+09 2704.686069 2240.879458 December
4274 2020-12-28 2884.040737 2923.386536 2876.112102 2903.267578 2108994 0.0 0.0 2020 6.082425e+09 2706.033939 2245.075523 December
4275 2020-12-29 2893.951334 2925.665869 2893.951334 2904.357666 1994151 0.0 0.0 2020 5.770976e+09 2708.194486 2249.433227 December
4276 2020-12-30 2908.222868 2921.404270 2876.111997 2883.346924 2637968 0.0 0.0 2020 7.671799e+09 2711.847599 2254.554294 December
4277 2020-12-31 2874.129861 2879.085257 2819.620501 2837.212158 4040956 0.0 0.0 2020 1.161423e+10 2714.345122 2259.646221 December

250 rows × 13 columns

In [39]:
#finding ROI in covid time
sumTCS1=0 #total amount invested in TCS during covid time

s2=0 #number of shares owned by TCS

#calcuating total amount invested and number of shares owned in TCS

for i in range(4028,4278):

    if df.loc[i,'Day']==30:

        sumTCS1+=df.loc[i,'Open']

        s2+=1

#displaying basic results

print("Total Invested in TCS = Rs",round(sumTCS1,2))

print("Shares Owned of TCS =",s2)

print("Average Investmentment of 1 share = Rs",round((sumTCS1/s2),2))



TCS_end=2874.12 #last open price of TCS on 2020-12-31


#calculating investment results

result2=round((TCS_end*s2)-sumTCS1,2)

ROI_TCS1=round((result2/sumTCS1)*100,2)


#displaying investment results

print("\nInvestment Result:")


if result2<0:

    print("Net Unrealised Loss = Rs",result2)

else:

    print("Net Unrealised Profit = Rs",result2)



print("TCS ROI from 2020-1-1 to 2020-12-31 =",ROI_TCS1,"%")

Total Invested in TCS = Rs 18083.81
Shares Owned of TCS = 8
Average Investmentment of 1 share = Rs 2260.48

Investment Result:
Net Unrealised Profit = Rs 4909.15
TCS ROI from 2020-1-1 to 2020-12-31 = 27.15 %
In [40]:
df2=df[df["year"]==2012]
df2['Month'] = pd.DatetimeIndex(df2['Date']).month_name()
df2

C:\Users\Dell\AppData\Local\Temp\ipykernel_13040\3432205665.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[40]:
Date Open High Low Close Volume Dividends Stock Splits year MarketCap MA50 MA200 Day Month
2063 2012-01-02 479.765471 490.075656 476.459593 487.038391 2248542 0.0 0.0 2012 1.078773e+09 464.288172 457.068325 2 January
2064 2012-01-03 488.959984 499.972700 487.616972 494.889893 2708884 0.0 0.0 2012 1.324536e+09 465.314653 457.267911 3 January
2065 2012-01-04 497.906595 497.947909 483.195447 484.724396 2386770 0.0 0.0 2012 1.188389e+09 466.676658 457.571421 4 January
2066 2012-01-05 484.641643 489.683086 482.389484 484.311035 2547312 0.0 0.0 2012 1.234533e+09 467.831059 457.800442 5 January
2067 2012-01-06 481.831625 491.067410 478.029887 483.236633 2119728 0.0 0.0 2012 1.021352e+09 468.773488 457.980715 6 January
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2303 2012-12-24 528.812311 532.625448 528.016149 530.572205 1254818 0.0 0.0 2012 6.635632e+08 539.583990 521.500739 24 December
2304 2012-12-26 530.907530 531.808449 526.004932 528.896179 1435258 0.0 0.0 2012 7.619893e+08 539.349917 521.621364 26 December
2305 2012-12-27 530.027528 530.027528 522.799309 524.182068 1994158 0.0 0.0 2012 1.056959e+09 539.197740 521.731373 27 December
2306 2012-12-28 525.460258 533.002749 524.098419 531.117126 1501622 0.0 0.0 2012 7.890427e+08 538.918684 521.843408 28 December
2307 2012-12-31 528.393107 530.320621 524.621864 526.235107 704170 0.0 0.0 2012 3.720786e+08 538.794025 521.992900 31 December

245 rows × 14 columns

In [41]:
#finding ROI in 2012
sumTCS2=0 #total amount invested in TCS during 2012

s3=0 #number of shares owned by TCS

#calcuating total amount invested and number of shares owned in TCS

for i in range(2063,2308):

    if df.loc[i,'Day']==30:

        sumTCS2+=df.loc[i,'Open']

        s3+=1

#displaying basic results

print("Total Invested in TCS = Rs",round(sumTCS2,2))

print("Shares Owned of TCS =",s3)

print("Average Investmentment of 1 share = Rs",round((sumTCS2/s3),2))



TCS_end=528.39 #last open price of TCS on 2012-12-31


#calculating investment results

result3=round((TCS_end*s3)-sumTCS2,2)

ROI_TCS2=round((result3/sumTCS2)*100,2)


#displaying investment results

print("\nInvestment Result:")


if result3<0:

    print("Net Unrealised Loss = Rs",result3)

else:

    print("Net Unrealised Profit = Rs",result3)



print("TCS ROI from 2012-1-1 to 2012-12-31 =",ROI_TCS2,"%")

Total Invested in TCS = Rs 4105.04
Shares Owned of TCS = 8
Average Investmentment of 1 share = Rs 513.13

Investment Result:
Net Unrealised Profit = Rs 122.08
TCS ROI from 2012-1-1 to 2012-12-31 = 2.97 %
In [42]:
plt.figure(figsize=(20,10))

year=['2002-2021','2012','2020']

ROI=[ROI_TCS,ROI_TCS2,ROI_TCS1]

col=['Blue','Grey','Orange']

plt.bar(year,ROI,color=col)

plt.title("ROI")

plt.xlabel("Time")

plt.ylabel("Percentage")
Out[42]:
Text(0, 0.5, 'Percentage')
In [43]:
plt.figure(figsize=(20,10))

stock=['2002-2021','2012','2020']
result=[result1,result3,result2]

col=['Blue','Grey','Orange']



plt.pie(result,labels=stock,autopct="%1.2f%%",colors=col)

plt.legend(title="",loc="upper left")

plt.title("Realised Profit")
Out[43]:
Text(0.5, 1.0, 'Realised Profit')

*Conclusion*:

This is NOT FINANCIAL ADVICE, and all work done in this project is for educational purposes only. This analysis depicts a stock’s long-term performance,and int covid time and shows the potential of SIP in the long run.
During our visualization we can say that share price of tcs will go on increasing untill and unless some big fundamental factors caused some problems.
But due to high demand and increasing interest in tech field chances of this stock giving good returns is excellent because it survived through the covid and gave a good return.

In [ ]: